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Introduction to Machine Learning in R: Part 2

This is a six-hour tutorial on machine learning in R that covers data preprocessing, cross-validation, ordinary least squares regression, lasso, decision trees, random forest, xgboost, and superlearner algorithms. These methods that are important across scientific disciplines for computational investigation of virtually all academic research questions and can help you gain an edge for employment in university, business, industry, and technology settings. 

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Introduction to Machine Learning in R: Part 1

This is a six-hour tutorial on machine learning in R that covers data preprocessing, cross-validation, ordinary least squares regression, lasso, decision trees, random forest, xgboost, and superlearner algorithms. These methods that are important across scientific disciplines for computational investigation of virtually all academic research questions and can help you gain an edge for employment in university, business, industry, and technology settings. 

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R Graphics with ggplot2

This workshop will provide an introduction to graphics in R with ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data. We will also explore the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or panelling by groups. You will learn how to make histograms, boxplots, scatterplots, lineplots, heatmaps, and geographic maps using the Google Maps API. You will also learn how to facet plots and create compound figures. 

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Talking about your research at Thanksgiving dinner

Posted: Nov, 12, 2019

By: Simal Ozen Irmak

Research is the biggest driving force behind innovation and success. It is also the main reason why many of us are drawn to higher education. It is the bread and butter of what we do and why we do it. It is the motivation behind all those underpaid hours we spend in front of our computers designing surveys, crunching numbers, reviewing the literature, making grant deadlines, tediously crafting and re-crafting study protocols, running experiments, going through zillions of revisions on our manuscripts… 

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Library Carpentry

General Information

Library Carpentry is part of the Carpentries, and is made by people working in library- and information-related roles to help you:

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D-Lab is Hiring GSRs!

Posted: Nov, 12, 2019

By: Patty Frontiera

The UC Berkeley D-Lab (Social Sciences Data Laboratory) is hiring up to five full-fee remission, 50% time appointment (20 hours/week), graduate student researcher (GSR) positions starting in January 2020 to teach workshops and provide consulting. These positions may be renewed. 

 

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Python Fundamentals Part 4

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application.

NOTE: This is not a quantitative workshop, but instead a how-to-use-Python workshop. However, you will find that after completing this series you will have the skills to code quantitative tasks. 

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Python Fundamentals Part 3

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application. 

NOTE: This is not a quantitative workshop, but instead a how-to-use-Python workshop. However, you will find that after completing this series you will have the skills to code quantitative tasks.  

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Python Fundamentals Part 2

This four-part, interactive workshop series is your complete introduction to programming Python for people with little or no previous programming experience. By the end of the series, you will be able to apply your knowledge of basic principles of programming and data manipulation to a real-world social science application. 

NOTE: This is not a quantitative workshop, but instead a how-to-use-Python workshop. However, you will find that after completing this series you will have the skills to code quantitative tasks. 

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Python Fundamentals Part 1

Part 1 Topics:

  • Running Python
  • Jupyter notebooks
  • Variables assignment
  • Types conversion
  • Strings
  • Built-ins

Knowledge requirements: Programming Fundamentals or equivalent prior knowledge

Registration note: To participate in multiple parts of this series, please be sure to register for each day separately.

Technology requirements: 

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